Pre-trained language models (PLMs) leverage chains-of-thought (CoT) to
simulate human reasoning and inference processes, achieving proficient
performance in multi-hop QA. However, a gap persists between PLMs' reasoning
abilities and those of humans when tackling complex problems. Psychological
studies suggest a vital connection between explicit information in passages and
human prior knowledge during reading. Nevertheless, current research has given
insufficient attention to linking input passages and PLMs' pre-training-based
knowledge from the perspective of human cognition studies. In this study, we
introduce a \textbf{P}rompting \textbf{E}xplicit and \textbf{I}mplicit
knowledge (PEI) framework, which uses prompts to connect explicit and implicit
knowledge, aligning with human reading process for multi-hop QA. We consider
the input passages as explicit knowledge, employing them to elicit implicit
knowledge through unified prompt reasoning. Furthermore, our model incorporates
type-specific reasoning via prompts, a form of implicit knowledge. Experimental
results show that PEI performs comparably to the state-of-the-art on HotpotQA.
Ablation studies confirm the efficacy of our model in bridging and integrating
explicit and implicit knowledge.

该研究引入了一个使用提示将显性和隐性知识连接起来的 PEI 框架，以实现与人类阅读过程相符的多跳问题回答。实验结果表明，PEI 在 HotpotQA 上表现与最先进的模型相当。消融研究证实了我们的模型在链接和整合显性和隐性知识方面的有效性。